Member of Technical Staff - Data Quality Engineer (Pre-training)
Why This Role Stands Out
This role offers a unique opportunity to directly influence the performance of cutting-edge AI models by ensuring the highest standards of data quality. You'll thrive here if you have strong engineering fundamentals and a passion for data, working alongside world-class researchers to shape the future of intelligence. Apply now to contribute to groundbreaking advancements in AI.
Quick Overview
Job Description
Our Mission
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
About the Role
Data is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data.
As a member of the Data Team, your mission is to ensure that the data used to train our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities.
Working with world-class researchers on our pre-training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model performance.
Working closely with our pre-training teams you will:
Own upstream data quality for LLM pre-training; as a specialist or generalist across languages and modalities
Partner closely with research and pre-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors
In addition to human-in-the-loop processes, you will design, validate, and scale automated QA methods to reliably measure data quality across large campaigns
Build reusable QA pipelines that reliably deliver high-quality data to pre-training teams for model training
Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria
About You
Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre-training data
Detail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality
Solid understanding of how data quality impacts pre-training, with the ability to translate quality concerns into concrete signals, decisions, and feedback
Experience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-Judge
Comfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partners
Skills and Qualifications
Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code
Experience working with large datasets and automated evaluation or quality-checking systems
Familiarity with how LLMs work and can describe how models are trained and evaluated
Excellent communication skills with the ability to clearly articulate complex technical concepts across teams
What We Offer:
We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.
We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.
Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
Meals: Lunch and dinner are provided in the office daily.
Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
Team building: We have regular off-sites, happy hours, and team celebrations.
Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization.
Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice
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